Abstract: Many experiments generate images with a large number of visual cues. These can be interpreted by clever and experienced science domain experts, but not by most automated image analysis pipelines. Changes in the instrument or in sample condition all lead to corresponding visual features in the images. How can we automate identification of patterns to separate the signal from the noise, the discovery from the artifact?

CLICK HERE to sign up for "office hours", to be held in Building 6, Room 2244